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335-340, 2012 Issn 1991-8178 Australian Journal of Basic and Applied Sciences, 6(12): 335-340, 2012 ISSN 1991-8178 Evaluating Efficiency of SEPAH Bank Branches in Mazandaran Province by Using Data Envelopment Analysis 1Ali Sorayaei, 2Fariborz Kalashi and 3Masoumeh Seifi Divkolaii 1Assistant professor & Faculty member of the Business Management department of Islamic Azad University of Babol , Babol, Iran 2Master of Management, MBA 3PHD student of media management, Imam reza international university, mashhad,Iran, Member of Young Researchers Club of Qaemshahr Abstract: Most banks to evaluate their efficiencies only consider profit criteria and to evaluate different aspects of operations they usually use multiple ratios. The fact is that analysis of financial ratios provides little information.Accordingly, in this study by applying data envelopment analysis technique, efficiency of different branches of Sepah Bank in Mazanadaran in the year of 2009 is investigated. The results show that among 50 evaluated branches, only 5 branches located in Amol, Sari, Haraz Amol, Pasdaran and Gharen have 100 percent total efficiency and also the average efficiency of all branches is about 80 percent. Additionally, dividing the efficiency scores into two categories, that is pure technical efficiency and management efficiency, shows that in only 14 cases the size of management inefficiency is higher than criterion inefficiency and in the rest of cases criterion inefficiency caused by non-optimality of the bank size is the main cause of being inefficient. Finally, by determining pattern units for inefficient branches, the way of reaching the frontier efficiency is introduced for each of these branches. Key words: Efficiency evaluation, Sepah Bank, Data envelopment analysis, Pattern Finding. INTRODUCTION All the endeavors of human are focused on gaining the maximum output by spending the least of those equipments which he has in hand. This tendency could be called reaching the higher efficiency. Efficiency is one of the important criteria for assessing the optimality of economic units and the basic step in enhancing efficiency is measuring it (Folan,2005: 663-680). Banking transactions are recognized worldwide as one of the prominent economic operations in each economical system. For any operation that requires gaining capital and economic resources, certainly one bank or financial organization must be involved. Generally speaking, banks can be known as part of financial system of society economics whose duty are easing transactions and paying and also as the largest financial intermediary they execute money policies for attracting savings, supplying businesses and performing projects and so forth(Bala,2003: 439 – 450). Banking system in Iran is very decisive because in addition to playing an intermediary role in money market, because of not developed capital market and also not developed organizations and tools, it plays an important role in bringing money to mid- term or long-term economic programs. At the same time that banks can be useful in economical promotion and development, their improper and inefficient operations can lead to financial crises. Long story short, a healthy banking system reflects the health of society economy .( Najafi,2005: 45-84)Accordingly, the current study aims at measuring efficiency of each branches of Sepah Bank in Mazandaran province so as to identify efficient branches, reasons for inefficiency and finding a solution for improving inefficient branches. Doing so, data envelopment analysis technique, which is based on linear programming methods and has found wide application in bank efficiency evaluation, has been applied. 2. Analytical Framework and Research Background: Lin et al. (2009) have evaluated 117 bank branches in Taiwan. The obtained results demonstrated that in terms of total efficiency entire banks have apparent inefficiencies such that average total efficiency was 54.8 percent and average of pure technical efficiency was 67 percent. At the end, researchers concluded that this issue is due to low proportion of loans to deposits which causes the resources to be wasted (Lin,2009: 8883– 8891). In another study, Chen et al. (2005) have evaluated 16 banks in China. They have considered capital and asset as input and net income, ROA and ROE as outputs. Results showed that only 2 banks had scale efficiency, 2 banks had constant efficiency, 7 banks had increasing efficiency and 7 banks had decreasing return to scale (Chen,2005: 229–245)Ahmadpour (2006) has investigated the efficiency of Saderat Bank branches across Mazandaran province. In his work, 141 branches of Saderat Bank were considered and their Corresponding Author: Ali Sorayaei, Assistant professor & Faculty member of the Business Management department of Islamic Azad University of Babol, Babol, Iran. E-mail: [email protected] 335 Aust. J. Basic & Appl. Sci., 6(12): 335-340, 2012 efficiencies were estimated using DEA techniques. In this study, the input variables included number of personnel, number of terminals and how much building of the branch costs and output variables were entire deposits, private sector facilities, and overdraft debts. The outcome of this research proves average efficiency is low (30 percent) in all the branches (AhmadPour,2006). The efficiency of Tejarat Bank supervision was evaluated by Dadgar and Niknemat (2007) through DEA. In their research 38 supervisions of Tejarat Bank were taken into consideration and efficiencies of units were measured applying two models CCR and BCC. The results showed that supervisions of regions No. three, four and five of Tehran are more efficient and supervisions of Qom, Zanjan, West Azerbaijan and East Azerbaijan are inefficient (Dadgar,2007: 11-45). Abrishami et al. (2003) have focused on evaluation of banking system efficiency with case study of Melat Bank during years of 1991 untill 2003. Parametric Econometric technique and random frontier cost Translog function were utilized to estimate the amount of cost efficiency. After estimating cost function and appearing inefficiency part it was revealed that ten percent of error model variation is because of inefficiency part. Moreover, the computations associated with cost efficiency showed that proportion of done total cost to minimum bank total cost is 1.07 averagely. Hence, Melat Bank in the above-mentioned period has merely faced seven percent of cost inefficiency.( Abrishami,2004: 173-193) 3. Research Methodology: In this paper, we evaluate the efficiency of Sepah Bank located in Mazandaran Province in 2009 via using data envelopment analysis approach. To do so, by using basic models of data envelopment analysis (BCC and CCR) and non-increasing return to scale model three types of efficiency, technical, pure and scale are calculated. The required computations are done by DEA-Solver software. The units, evaluated in this paper, are homogenous from production process’s perspective. Their inputs are personnel number, terminal number and operational costs and also their outputs include average facilities and average deposits. 3.1. Data Envelopment Analysis: Data envelopment analysis is a method based on mathematical programming which for measuring relative efficiency of each decision making unit (DMU), having several inputs and outputs, develops an efficient frontier (Cook,2009: 1–17). In this method the efficiency of DMUi is introduced as the proportion of weight summation of inputs to weight summation of outputs. In this course, DEA looks for the most proper weight sets for each DMUi. These weight sets maximize efficient ratio of DMUi providing that this ratio does not violate 1 for any of the units. 3.1.1. CRR Model: Charnes et al. (1978) under Constant Return to Scale (CRS) assumption have proposed a model which tries to envelop data by generating an experimental frontier; thus, the technical efficiency of each unit was estimated based on this frontier Figure 1 (Charnes,1978: 429-444) Figure 1 depicts a system with one input and one output. Here, to calculate efficiency of unit , first value of Y is assumed to be constant then efficiency in the shape of OF in the CCR model or OA in the BCC model is calculated. OE OE The main issue in DEA models is determining weights ur and vi such that the amount of DMU efficiency be maximized. Let there are decision making units and DMUj consumes an -tuples vector input and generates an -tuples vector output. Therefore, the efficiency of DMU0 is calculated via model 1. To calculate the efficiency of decision making units, model 1 should be solved times. Since the model is nonlinear, to deal with it methods such as fraction programming, fixing the denominator of objective function (input-oriented models) or fixing the numerator of objective function (output-oriented models) are proposed. Once the model was solved, a unit is efficient if the value of objective function is 1 and also all the ur and vi are positive. Therefore, the values of these variables in model (1) are considered to be equal or greater than a small number like ( ). A unit with the above conditions is called strong efficient unit. A unit is called weak when the value of objective function is 1 but value of at least one of ur or vi is zero. Since the condition of being positive for variables is imposed on model (1), this model is not capable of distinguishing weak efficient from inefficient units; therefore, these two groups are classified as inefficient units. The models discussed in the data envelopment analysis are of linear programming models; hence, these models have another form named dual models (envelopment form) where for example the envelopment form of BCC model is presented (model 3). The CCR model is one of the Constant Return to Scale models. These 336 Aust. J. Basic & Appl. Sci., 6(12): 335-340, 2012 models ignore the value of decision making unit when evaluating the performance despite the probable effect of institution size on producing services with more efficiency. Fig. 1: Efficiency frontier in BCC and CCR models. s ur yr0 Max Z r1 0 m vi xi0 i1 st : s ur yrj r1 m 1 vi xij i1 u ,v r i Model 1.
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